Dear Ashley, Thank you for submitting your proposal. A printable summary is below. Your confirmation number is 9439. A confirmation email will be sent to you within 24 hours. Applicants will be notified of the status of the proposed project on May 4, 2015. If you have questions or need assistance regarding your application please contact the AIR Grant staff at 850-385-4155 x200 or [email protected]. SUMMARY Personal Information Name Ashley Brooke Clayton Informal Name Ashley Affiliation North Carolina State University Unit/Department Leadership, Policy, and Adult and Higher Education Title Graduate Research Assistant Year began this position 2012 Email [email protected]Alternate Email [email protected]Cell Phone 7578106092 Preferred Mailing Address 2203 Lakeside View Court Cary, North Carolina 27513 United States Phone: 757.810.6092 Secondary Address North Carolina State University 608F Poe Hall/ Campus Box 7801 Raleigh, North Carolina 27695-7801 United States Phone: 919-513-2514 Demographics Highest degree Discipline of highest degree Position description Staff members in IR office Campus type Years of experience in IR IR Roles Year of birth Race/Ethnicity Gender Grant Type I am applying for a: Dissertation Grant Page 1 of 9 Summary 3/26/2015 https://apps.airweb.org/applicationprocess/Summary.aspx?aid=e94757c4-8736-e311-8c20-...
Transcript
Dear Ashley,
Thank you for submitting your proposal. A printable summary is below. Your confirmation number is 9439. A confirmation email will be sent to you within 24
hours.
Applicants will be notified of the status of the proposed project on May 4, 2015.
If you have questions or need assistance regarding your application please contact the AIR Grant staff at 850-385-4155 x200 or [email protected].
SUMMARY
Personal Information
Name Ashley Brooke Clayton
Informal Name Ashley
Affiliation North Carolina State University
Unit/Department Leadership, Policy, and Adult and Higher Education
Effects of College Counselors on College Access: An Inverse Probability Weighting Analysis
Statement of the research problem and national importance (limit 750 words):
• What is the research problem this proposal intends to address?
• Why is this topic of national importance?
• Why is it timely to conduct this research at this time?
Research Problem
Although strides have been made to promote more equity in college access, many populations, particularly low-income, first-generation, and
ethnic/racial minorities, are still highly underrepresented in higher education (Perna & Kurban, 2013; Ross et al., 2012). One contributor to this
enrollment problem is that many high schools, especially those with low college-going rates, lack sufficient college-related counseling (McDonough,
2005a; Perna et al., 2008). Public schools typically lack a designated staff member that has college preparatory responsibilities as their primary job and
who is accountable for college enrollment (McDonough, 2005b). Studies have found that supportive school counselors can be especially influential in
helping students with the college search and application process (Hossler, Schmidt, & Vesper, 1999; McDonough, 1997). However, the availability and
nature of college counseling varies greatly across and within schools (Linnehan, 2006; Perna et al., 2008). College counseling is less available in schools
with predominantly minority and/or low-income populations (McDonough, 2005a), whereas private preparatory schools invest significant resources in
their college counseling operations (McDonough, 2005b).
By default, high school guidance counselors are often tasked with assisting students with college aspirations; however, many counselors are overworked
and have other competing priorities (McDonough, 2005b). According to Perna and Kurban (2013), “Especially in low-performing high schools,
counselors often have numerous other noncollege-related responsibilities, including scheduling, testing, and providing personal and nonacademic
counseling, and may not be trained in the nuances of college and financial aid processes” (p. 22). Further, training in school counseling has not
historically included preparation in college counseling specifically (McDonough, 2005b). Therefore, as guidance counselors lack both time and training
to devote to assisting students with the college choice process, schools might need additional staff who can be devoted solely to college counseling.
In recent years there has been a development of new initiatives focused on counseling, coaching, and advising students in the college choice process
(Stephan & Rosenbaum, 2013). Some high schools have added college counselors, whose primary responsibility is assisting students with the college
choice process. These individuals serve as resources for students above and beyond the traditional guidance counselors. While many studies have
addressed college-preparatory programs, there is a smaller group of studies that examine the role of college counseling in particular (e.g. Avery, 2010;
Castleman & Goodman, 2014). As new approaches to college enrollment are developed, such as adding high school college counselors, it is critical that
rigorous impact evaluations of the interventions’ effect are conducted, as this will help inform future policy.
The purpose of this study is to explore the effect of having a college counselor in a public high school on three primary college access outcomes:
college applications, FAFSA completion, and postsecondary enrollment. Using inverse probability weighting, this study will compare the postsecondary
application and enrollment outcomes of students who attended public high schools with a college counselor to a comparison group who did not have
this resource. This study will be the first study that examines the causal impact of college counseling on postsecondary outcomes using a nationally
representative sample of students.
Implications of National Importance
In recent years, there has been an increased investment in college counseling initiatives; however, little is known about the effects of specialized college
counselors. This study is of national importance, as the findings will help inform policy for public high schools. College enrollment of high school
graduates is currently not built into the K-12 accountability system (McDonough, 2005b), which is evidenced by the lack of college counseling in public
schools as compared to private schools. As the majority of private high schools invest in college counseling initiatives and only about one in four public
high schools have a college counselor (McDonough, 2005b; NACAC, 2011), it is important to gain an understanding of the unique role and effectiveness
of college counselors in public high schools to see which practices can be replicated on a larger scale. Public schools often have limited resources, and
this study could help inform where to best allocate financial resources. Further, this study could provide implications for federal and nonprofit funding
of college counseling initiatives. The Department of Education and other non-profit organizations, such as the College Advising Corps, will have
research findings that could potentially support funding of college counselors in high schools. Lastly, this study will have important implications for
counselor education graduate programs, as they might consider developing more specialized programs for college counseling.
Review the literature and establish a theoretical grounding for the research (limit 1000 words):
• What has prior research found about this problem?
• What is the theoretical/conceptual grounding for this research?
College Counseling
Central to this study is college counseling and how this assistance impacts college enrollment. Two major bodies of literature focus on college
counseling in public high schools. The majority of prior studies focus on the role of traditional guidance counselors and a smaller group of studies
examines the role of specialized college counselors in the high school context.
Guidance counselors. Many research studies focus on college counseling in the context of public high school counselors (Linnehan et al., 2006;
McDonough, 1997, 2002, 2005a, 2005b; McDonough & Calderone, 2006; McKillip et al., 2012; Perna et al., 2008; Venezia & Kirst, 2005). According to
McDonough (2005b), “Counselors are the logical choice to be the K-12 staff member responsible for college access preparation and assistance and are
often assumed to be handling this role, yet they are inappropriately trained and structurally constrained from being able to fulfill this role in public high
schools” (p. 69). Historically, educational programs for school counselors have not specifically included training in college counseling (Hossler et al.,
1999; McDonough 2002, 2005b). In addition to having little training, guidance counselors also have limited time to devote to college counseling given
their large student loads (McDonough, 2005a, 2005b). For example, while the American School Counselor Association (2011) recommends a maximum
student-to-counselor ratio of 250:1, in the 2010-2011 school year, the national average in public schools was 471:1. Further, guidance counselors “often
have numerous other noncollege-related responsibilities, including scheduling, testing, and providing personal and nonacademic counseling, and may
not be trained in the nuances of college and financial aid processes” (Perna & Kurban, 2013, p. 22).
Studies have found that supportive school counselors can be especially influential in helping students with the college search and application process
(Hossler, et al., 1999; McDonough, 1997). However, the nature of college counseling services varies greatly across and within schools (Linnehan et al.,
2006; Venezia & Kirst, 2005). Specifically, college counseling is more common for students in advanced college preparatory tracks (McDonough, 2005a;
Venezia & Kirst, 2005) and of higher socioeconomic status (Linnehan et al., 2006). Further, college counseling is less available in schools with
their college counseling operations (McDonough, 2005b).
College counselors. There is very limited research on college counselors within the high school context. While there is a significant amount of literature
on college access, choice, and guidance counseling, there is little research that focuses specifically on how the addition of a college counselor in a high
school impacts college access. There have been a few initiatives that fall under the “coaching” model, in which a college advisor or counselor is assigned
to a high school to assist students with the college enrollment process (Stephan & Rosenbaum, 2013). One of the largest initiatives of this type is the
National College Advising Corps, which places recent college graduates into high-need public high schools to serve as college advisors (National
College Advising Corps [NCAC], 2014). In one county in North Carolina, schools who added a college advisor saw an increase in college attendance of
approximately 14 percentage points compared to control schools in the same county (Carolina College Advising Corps [CCAC], 2012).
Another similar intervention is the college coach program in Chicago Public Schools (Stephan & Rosenbaum, 2013). Coaches were not randomly
assigned to high schools, although they were “distributed fairly evenly across high schools in terms of socioeconomic composition, racial composition,
and academic achievement” (Stephan & Rosenbaum, 2013, p. 204). Using a difference-in-differences design, this study found that compared to schools
without a college coach, the coached schools had greater gains in college enrollment. Specifically, schools with the college coach treatment had
increased college enrollment by 1.7 percentage points, increased college applications by 4.7 percentage points, and increased FAFSA completion by 2.6
percentage points compared to non-coached schools (Stephan & Rosenbaum, 2013).
Despite these two related studies, there are currently no national studies on public school college counselors. The Chicago Public Schools study is the
most similar to my proposed study as it examines the impact of a college counselor in a high school on the same college access outcomes: college
applications, FAFSA completion, and college enrollment. This study seeks to expand the literature on college counseling in high schools by examining
this intervention on a national sample of high school students.
Theoretical Framework
This study is framed by Perna’s (2006) multilevel conceptual model of college choice. The model is based on an extensive review of prior research
addressing students’ college choice and enrollment behaviors. Central to the model is human capital theory, which assumes that students’ compare the
expected benefits with expected costs when making their college decisions (Becker, 1993). Perna’s model builds on human capital and assumes that
four contextual layers also influence an individual’s college choice decision. The four layers of the model are: (1) the individual’s habitus; (2) the school
and community context; (3) the higher education context; and (4) the broader social, economic, and policy context. This study draws primarily on the
school context layer, as the college counselor can be viewed as a school resource and support for students. Layer 2 is based on McDonough’s (1997)
concept of “organizational habitus,” which identifies ways that schools and communities facilitate or impede the college choice process. School
personnel can be influential in providing access to resources and helping students navigate the college application process (Hossler et al., 1999;
McDonough, 1997; Stanton-Salazar, 1997). The college counselor in this study is a school resource that should facilitate the college choice process for
students who have access to this treatment.
Describe the research method that will be used (limit 1000 words):
• What are the research questions to be addressed?
• What is the proposed research methodology?
• What is the statistical model to be used?
Research Questions
This study will ask three research questions to understand the effects of attending a public high school with a college counselor:
1. To what extent does having a college counselor in a high school have an effect on the number of college applications students complete?
2. To what extent does having a college counselor in a high school have an effect on students’ completion of the FAFSA?
3. To what extent does having a college counselor in a high school have an effect on students’ postsecondary enrollment?
Variables
Variables for the study are selected from three waves of HSLS:09 (see Table 1). There are two questions on the 2012 First Follow-up that address the
college counselor treatment. The first college counselor treatment variable, C2CLGAPP, addresses assistance with college applications. The survey
question asks, “Does your school have one or more counselors whose primary responsibility is assisting students with college applications?” The second
college counselor treatment variable, C2SELECTCLG, addresses assistance with college selection. The survey question asks, “Does your school have one
or more counselors whose primary responsibility is assisting students with college selection?” The third treatment group in this study will include
students that had access to at least one of the first two treatments.
Three outcome variables were chosen to answer the three research questions. First, the variable S3CLGAPPNUM indicates how many college
applications a student completed. Second, the variable S3APPFAFSA is a dichotomous variable that indicates whether or not a student completes the
Free Application for Federal Student Aid (FAFSA). Lastly, the third outcome focuses on postsecondary enrollment. The variable, S3CLASSES, indicates if
the student was enrolled in postsecondary level classes as of November 1, 2013.
Finally, selection of covariates is fundamental to having a strong statistical model. I will select covariates based on prior research findings and logical
explanations for what drives selection into treatment. Variables will be selected that affect both the selection of having a college counselor and the
outcome variables. Each selected covariate is aligned with theory and past empirical findings. Notably, two levels of selection are taking place in this
study, as schools are selecting to have counselors whose primary responsibility is assisting students with college and students are selecting into high
schools with or without the college counselor resource. Therefore, both school-level and student-level covariates will be in the matching model (see
Tables 2 & 3).
Methodology
Inverse probability weighting, a type of propensity score analysis, will be used to analyze the effects of the college counselor treatment. This study seeks
to compare students that are similar to the treatment group on all relevant pretreatment characteristics X, as determined by the probability (propensity)
of having the treatment. The first step of the design phase is to estimate the probability of treatment. Each unit will have a propensity score, which is the
predicted probability of treatment. After the first step, I then create weights based on the inverse probabilities of the propensity model. One of the
major advantages of using inverse probability weighting is that the analysis can include the entire analytical sample (except for the trimmed tails).
Further, using inverse probability weighting allows the researcher to run different analyses by simply the weights to the regression equation.
Statistical Models
College application model. The first empirical model will test the effects of the three college counselor treatments on the number of college applications
a student completes (see Figure 2). This analysis will use a Poisson regression equation, which is a standard approach when working with count data
(Greene, 2008). The Poisson regression model for this analysis can be depicted as:
where the predicted S3CLGAPPNUM i is the predicted number of applications for an individual with the COLLEGECOUNSELOR treatment. In this model,
this special transformation function is called the link function, which is the natural log (ln) depicted in the equation (Coxe et al., 2009). The regression
coefficient 1 indicates if the COLLEGECOUNSELOR treatment has an effect on the number of college applications completed. This analysis will control
for state fixed effects δi. Lastly, the calculated weights are added to the model.
FAFSA completion model. The second empirical model will examine the effects of the three college counselor treatments on FAFSA completion (see
Figure 3). A linear probablity model will be used given that the outcome variable of FAFSA completion is binary. The following is the prediction equation
The variable, S3APPFAFASAi is the predicted outcome variable for FAFSA completion for each individual in the analysis. Importantly, this regression
equation will capture the treatment effect in the coefficient of the treatment variable, COLLEGECOUNSELOR. This analysis will also control for state fixed
effects (δi). The error term, εit, captures all other factors, which influence the dependent variable that are not accounted for in the model. The previously
calculated inverse probability weights are added to the model.
College enrollment model. The last empirical model, and arguably the most important, will examine the effects of the college counselor treatment on
college enrollment (see Figure 4). This third model will employ a linear probability strategy to examine the effects on the outcome variable of college
enrollment, S3CLASSES. The collapsed variable will be a binary outcome indicating if the student was enrolled in postsecondary courses of any type in
the fall following the expected high school graduation year. The following is the prediction equation for the college enrollment model:
The variable, S3CLASSESi is the predicted outcome variable for postsecondary enrollment for each individual in the analysis. This regression equation
will capture the treatment effect in the coefficient of the treatment variable, COLLEGECOUNSELOR. This analysis will also control for state fixed effects
(δi). The error term, εit, captures all other factors which influence the dependent variable that are not accounted for in the model. Lastly, weights are
added to the model to properly weight each observation.
References cited (no word limit):
References
American School Counselor Association (2011). Student-to-school counselor ratio 2010-2011. Retrieved from
Avery, C. (2010). The effects of college counseling on high-achieving, low-income students (Working Paper No. 16359). National Bureau of Economic
Research.
Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education. Chicago: University of Chicago Press.
Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of application assistance and information in college decisions: Results
from the H&R Block FAFSA Experiment. The Quarterly Journal of Economics, 127(3), 1205-1242.
Castleman, B. L., Arnold, K., & Wartman, K. L. (2012). Stemming the tide of summer melt: An experimental study of the effects of post-high school
summer intervention on low-income students’ college enrollment. Journal of Research on Educational Effectiveness, 5(1), 1-17.
Castleman, B., & Goodman, J. (2014). Intensive college counseling and the college enrollment choices of low income students (Working Paper Series No.
Coxe, S., West, S. G., & Aiken, L. S. (2009). The analysis of count data: A gentle introduction to Poisson regression and its alternatives. Journal of
Personality Assessment, 91(2), 121-136.
Greene, W. H. (2008). Econometric analysis. 5th ed. Upper Saddle River, NJ: Prentice Hall.
Hossler, D., Schmit, J., & Vesper, N. (1999). Going to college: How social, economic, and educational factors influence the decisions students make.
Baltimore, MD: Johns Hopkins University Press.
Linnehan, F., Weer, C. H., & Stonely, P. (2006). High school guidance counselors: Facilitator or preemptors of social stratification in education. Paper
presented at the annual meeting of the Academy of Management, Atlanta, GA.
McDonough, P.M. (1997). Choosing colleges: How social class and schools structure opportunity. Albany: State University of New York Press.
McDonough, P. M. (2002). High school counseling and college access: A report and reconceptualization. Oakland: University of California, Office of the
President, Outreach Evaluation Task Force.
McDonough, P. (2005a). Counseling and college counseling in America’s high schools. In D.A. Hawkins and J. Lautz (Eds.), State of college admission (pp.
107-121). Washington, DC: National Association for College Admission Counseling.
McDonough, P. (2005b). Counseling matters: Knowledge, assistance, and organizational commitment in college preparation. In W. Tierney, Z. B. Corwin,
& J. E. Colyar (Eds.), Preparing for college: Nine elements of effective outreach (pp. 69-87). Albany: State University of New York Press.
McDonough, P. M., & Calderone, S. (2006). The meaning of money: Perceptual differences between college counselors and low-income families about
college costs and financial aid. American Behavioral Scientist, 49(12), 1703-1718.
McKillip, M. E., Rawls, A., & Barry, C. (2012). Improving college access: A review of research on the role of high school counselors. Professional School
Counseling, 16(1), 49-58.
National Association for College Admission Counseling (NACAC). (2011). State of college admission, 2011. Washington, DC: Author.
Perna, L. W. (2006). Studying college access and choice: A proposed conceptual model. In J. C. Smart (Ed.), Higher education: Handbook of theory and
research (Vol. 21, pp. 99–157). Dordrecht, NL: Springer.
Perna, L. W., & Kurban E. R. (2013). Improving college access and choice. In L.W. Perna & A.P Jones (Eds.). The state of college access and completion:
Improving college success for students from underrepresented groups (pp. 34-56). New York: Routledge.
Perna, L. W., Rowan-Kenyon, H. T., Thomas, S. L., Bell, A., Anderson, R., & Li, C. (2008). The role of college counseling in shaping college opportunity:
Variations across high schools. The Review of Higher Education, 31(2), 131-159.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Ross, T., Kena, G., Rathbun, A., KewalRamani, A., Zhang, J., Kristapovich, P., & Manning, E. (2012). Higher education: Gaps in access and persistence study
(NCES 2012-046). U.S. Department of Education, National Center for Education Statistics. Washington, DC: Government Printing Office.
Stanton-Salazar, R. D. (1997). A social capital framework for understanding the socialization of racial minority children and youths. Harvard educational
review, 67(1), 1-41.
Stephan, J. L., & Rosenbaum, J. E. (2013). Can high schools reduce college enrollment gaps with a new counseling model? Educational Evaluation and
Policy Analysis, 35(2), 200-219.
Venezia, A., & Kirst, M. W. (2005). Inequitable opportunities: How current education systems and policies undermine the chances for student persistence
and success in college. Educational Policy, 19(2), 283-307.
Project Description - Appendix
• Project Description - Appendix (Clayton)
NSF Datasets
NSF datasets:
Will you use a NSF dataset?
No
Please check all NSF datasets that apply:
Explain why the selected NSF dataset(s) best serves this research limit (250 words):
Include a variable list for each dataset used.
NCES Datasets
NCES datasets:
Will you use a NCES dataset?
Yes
Please check all NCES datasets that apply:
• High School Longitudinal Study of 2009 (HSLS:09)
Explain why the selected NCES dataset(s) best serves this research (limit 250 words):
Include a variable list for each dataset used.
This study will use a longitudinal dataset to examine the effects of college counseling on several college access outcomes. Data will be used from the
2009 High School Longitudinal Study conducted by the National Center for Education Statistics (NCES). This nationally-representative dataset includes
approximately 24,000 ninth graders from 944 high schools in the fall of 2009. An average of 25 students were randomly selected from the sample of
high schools to participate in the study. The First Follow-up was conducted in the spring of 2012 when most students were in their junior year of high
school. Most recently, the 2013 Update was collected to record students’ postsecondary options and plans (survey was administered from June –
December 2013). This study will examine students who are at public high schools and will not include private schools. The analytical sample will include
approximately 80 percent (or 19,000) of the students in the HSLS:09 study, who attended a public high school.
Prior national longitudinal surveys, such as the Educational Longitudinal Study of 2002 (ELS:2002) and the High School and Beyond (HS&B) study, did
not specifically ask if there was a counselor in the high school whose primary responsibility was college counseling. Many surveys ask questions about
traditional guidance counselors, yet they do not ask if there is a specific college counselor treatment. Therefore the HSLS:09 dataset best serves this
research, as it contains two questions that address the presence of a specialized college counselor in the high school.
Timeline and Deliverables
Timeline:
Provide a timeline of key project activities.
March 2015:
Defended dissertation proposal on March 4
Submit Institutional Review Board “Request for Exemption”
Apply for restricted-use license (Advisor already has a license)
April 2015:
Clean data and run descriptive statistics
Obtain HSLS:09 2013 Follow-up results (currently in the data processing and review stage)
May – July 2015:
Run preliminary analyses and models
August – October 2015:
Run final analyses and models
November 2015:
Beginning writing up results/findings
Submit mid-year report by November 4
Poster session of preliminary results at 2015 ASHE Annual Conference
December 2015:
Finalize results/findings section
January 2016:
Finalize and write-up discussion/conclusion section
February 2016:
Final Dissertation Defense
March 2016:
Final edits and submission to the North Carolina State Graduate School
April – May 2016:
Submit manuscript to academic journal
Work on 2016 AIR presentation
June 2016:
Presentation at 2016 AIR Annual Forum
Deliverables:
List deliverables such as research reports, books, and presentations that will be developed from this research initiative.
1. Research Report: findings and implications for college counseling policy and practice
2. Journal Article: will submit manuscript to a peer-reviewed academic journal
3. Presentations: 2016 AIR Annual Forum, 2015 Association for the Study of Higher Education (ASHE) Annual Meeting
Disseminate results:
Describe how you will disseminate the results of this research.
(Note: Costs of travel to meetings should be calculated on the budget page.)
The results of this study will be disseminated to academic, practitioner, and federal government audiences. First, the research findings from this study
will be submitted for publication in one of the following top tier educational journals: American Educational Research Journal, Journal of Higher
Education, or Research in Higher Education. In addition to presenting my findings at the 2016 AIR Annual Forum, I would also like to present at the
Annual Conference of the Association for the Study of Higher Education (ASHE). Further, a research report will be developed and shared with the
American School Counselor Association (ASCA), the Council for Accreditation of Counseling and Related Educational Programs (CACREP), and the Office
of Secondary Education at the U.S. Department of Education.
IRB Statement
Statement of Institutional Review Board approval or exemption (limit 250 words):
As part of the proposal, a statement outlining a plan for Institutional Review Board (IRB) approval is required. The statement should outline the applicant’s
timeline and plan for submitting the proposal to an IRB or explain why IRB approval is not necessary. Final IRB action is not necessary prior to submitting
the application.
This dissertation study will be considered "exempt" research. I will submit a "Request for Exemption" application to the Institutional Review Board at
North Carolina State University. This exemption application will be submitted to IRB by the end of March 2015.
Restricted Datasets
Statement of use of restricted datasets (limit 250 words):
Applicants should provide a statement indicating whether the proposed research will require use of restricted datasets. If restricted datasets will be used,
the plan for acquiring the appropriate license should be described. Review the requirements for restricted use licenses at the NCES and NSF websites.
If restricted datasets will not be used, leave this text box blank and click Save and Continue.
This proposed research will require the restricted-use HSLS:09 data. My advisor, Paul Umbach, has already applied for the restricted-use license and will
have the data soon. Students are not able to apply directly for a restricted-use data license. I April 2015, Paul Umbach will sponsor me by submitting an
application for a license. I will be considered an authorized user on his license.
Biographical Sketch(es)
Biographical sketch (limit 750 words):
Ashley Clayton is a Ph.D. candidate in the Educational Research and Policy Analysis program at North Carolina State University with a specialization in
Higher Education Administration. After completing her Bachelor’s degree at Virginia Tech in 2005, she served as an Admissions Advisor in the Office of
Undergraduate Admissions for two years. Desiring to further her career in higher education, Ashley earned a M.S. in Higher Education Administration
from Florida International University in 2009. During her time at FIU she served as a graduate assistant in the Career Services Office and Pre-College
Programs Office. In 2009, she accepted a position as the Academic/Career Coordinator of Upward Bound at Roanoke College, where she assisted first-
generation high school students with college applications and enrollment. After working with Upward Bound for several years, she enrolled full-time in
NC State’s Higher Education program in 2012. Ashley currently holds a graduate research assistantship in the Department of Leadership, Policy, and
Adult & Higher Education, where she assists faculty members on various research projects. She also serves as an Editorial Assistant for the Journal of
Higher Education.
Ashley has had a high level of quantitative training during doctoral studies. She has taken a foundational research course and both required levels of the
applied quantitative methods series. Last year, she took an advanced quantitative course on quasi-experimental methods, where she learned about
propensity score matching, inverse probability weighting, fixed effects, and regression-discontinuity techniques. She is currently taking a data
management course, where she is learning strategies for effectively working with large-scale quantitative data for educational research. The topics
covered in this course include: data cleaning, recoding and checking, merging and reshaping data, writing programs and macros to reduce errors, and
presenting descriptive data through tables and graphs. Given Ashley’s training in quantitative methods, she has tutored several doctoral-level students
in quantitative methods. This semester, Ashley was selected by the Associate Dean of the College of Education to serve as a Teaching Assistant for the
doctoral-level regression course.
During her three years at NC State, Ashley has collaborated with several faculty members on research projects and papers. She has worked as a
graduate research assistant for Drs. Andrew McEachin, Stephen Porter, and Paul Umbach. She has worked with her advisor, Paul Umbach, on several
research projects examining the effects of pre-college initiatives and remediation. Further she has collaborated with Stephen Porter on research that
examines the prevalence and implementation of field experiments in higher education. Her work with Andrew McEachin explores the effects of required
8th grade Algebra on several K-12 outcomes. Throughout these projects, Ashley gained significant experience working with large-scale datasets, writing
research papers, and presenting at national conferences. In the past three years, she has presented her research at the annual conferences of the
Association of Institutional Research (AIR), Association for the Study of Higher Education (ASHE), and the Association for Education Finance and Policy
(AEFP).
Ashley has substantial experience working with large datasets and national data from the National Center for Education Statistics. She has worked on
several projects where she has cleaned the data and run the analyses. Ashley has worked with several surveys from the Integrated Postsecondary Data
System (IPEDS) to examine the effects of North Carolina’s College Application Week on several institutional admissions outcomes. This project involved
merging, appending, and cleaning the various IPEDS surveys across several years. Recently she worked on a project using the Educational Longitudinal
Study, which examines the effects of college remediation on educational and labor market outcomes. Both of these papers have been presented at
national conferences in the past year. In addition to these projects using national data, she also has experience working with student-level data for the
University of North Carolina System and will be leading a project this spring that explores the effect of remediation at a single community college using
a regression-discontinuity approach. Ashley has also started working with the publicly available data from the High School Longitudinal Study of 2009
(HSLS:09) for her dissertation research. She ran some descriptive statistics and exploratory analyses to prepare for her dissertation proposal in March
2015. These experiences have given her valuable applied data analysis skills and made her proficient at handling issues related to large-scale national
survey data, such as weighting, missing data, and design effects.
Budget
• Dissertation Grant Budget Form - Ashley Clayton
Funding History
Funding history (limit 250 words):
A statement of prior, current, and pending funding for the proposed research from all sources is required. The statement should also include a history of
all prior funding from AIR to any of the PIs for any activity. Funding from other sources will not disqualify the application but may be considered in the
funding decision.
I have applied for two other dissertation grants and will be notified about the awards in the next two months.
I have applied for the following (pending funding):
1. AERA Dissertation Grant (up to $20,000) - awards announced in March 2015
2. NC State College of Education Dissertation Support Grant (up to $1,500) - awards announced in April 2015
